PumasAI Releases Pumas 2.9 Software Package
The update adds multithreading, distributed computing and expanded modeling features to the pharmacometrics platform.
Updated on Nov. 6, 2026 in Software
PumasAI released Pumas 2.9, a scientific software package for clinical pharmacology and pharmacometrics, building on the prior Pumas 2.8 release. The update includes enhancements to computational performance, estimation stability and reproducibility.
Why it matters
The release aims to help scientists work through increasingly complex pharmacometric problems without adding unnecessary complexity to their workflows. Faster simulations and more reproducible inference across parallel computing environments could ease the analytical burden on drug development teams.
The announcement is a vendor release notice rather than independent testing, so no performance benchmarks are available. Specific gains in simulation speed and estimation stability remain unquantified in the source material.
The players
PumasAI
PumasAI is an AI-powered clinical pharmacology and pharmacometrics company that develops the Pumas software platform for drug development modeling.
The details
The Pumas 2.9 update adds new multithreading capabilities and distributed computing enhancements with intelligent load balancing. It also extends support for ordered parameter domains, enhanced covariate selection, richer prediction outputs and expanded error model capabilities, along with improved diagnostics and clearer documentation.
Timeline
Pumas 2.9 was released on November 6, 2026.
The Tech Race
Pumas 2.9 extends the platform's prior 2.8 release by adding multithreading, distributed computing and experimental estimation method improvements. The update continues a pattern of incremental releases aimed at scaling pharmacometric modeling across parallel computing environments.
For researchers and developers using Pumas, the update adds multithreading and distributed computing options that may shorten simulation and estimation runtimes. Users will also gain richer prediction outputs and clearer diagnostics to support reproducible analyses.
The takeaway
Tools that improve reproducibility and parallel performance can reduce friction in complex modeling work. Teams evaluating pharmacometrics software may want to review release notes for features that match their computational needs.
Further reading
Explore more software coverage for context on how scientific computing tools are evolving.
Source note: This article includes information reported by Scientist Live.
